JP5816212B2 - Navigation server and navigation method - Google Patents

Description

  The present invention relates to a navigation server and a navigation method.

  Information on fuel consumption for each vehicle speed of gasoline vehicles is collected, and in addition to the collected information, the vehicle speed for each road obtained from the road traffic information and the road gradient obtained from the map information are taken into account. A technical method for predicting fuel consumption has been proposed (see Patent Document 1).

Japanese Patent No. 4401420

  However, the relationship between road gradient and vehicle fuel efficiency is not uniform. For example, on a downhill road, the fuel consumption is generally low because the required propulsive force of the vehicle is small. However, the frequency of acceleration / deceleration increases because of poor visibility or many curves. On the downhill, fuel consumption may increase. On the uphill, the fuel consumption is generally increased because the required propulsive force of the vehicle increases. However, the frequency of acceleration / deceleration is likely to be low due to a good prospect or a gentle curve. On uphill slopes, fuel consumption may be low.

  For this reason, even if map information including road gradient information is used for prediction of power consumption or power consumption of a vehicle having an electric motor for driving wheels such as an electric vehicle, the prediction accuracy may be lowered.

  Therefore, an object of the present invention is to provide a navigation server or the like that can create map information that can improve the prediction accuracy of energy consumption for driving a vehicle wheel.

The navigation server of the present invention is a navigation server configured to create map information including link characteristics specific to a link for at least one link among a plurality of links constituting a road, based on the energy consumption for the driving situation and the wheel drive of the vehicle is specified based on the collected travel records from the at least one is actually traveling or running vehicle links, wherein at least one of One or more reference defining elements configured to define an energy curve and a reference energy curve representing a correlation and a reference correlation between a driving situation of the vehicle of the same type as the vehicle at the link and an energy consumption amount, respectively. The deviation of the energy curve with respect to the reference energy curve in the designated driving situation of When the deviation is negative, it is defined as the link characteristic of the at least one link that is downhill, while when the deviation is positive, the uphill is defined as the link characteristic of the at least one link. A link characteristic defining element configured to be defined as a link characteristic.

The navigation method of the present invention is a method of creating map information including link characteristics specific to a link for at least one link among a plurality of links constituting a road, wherein the at least one link is actually used. based on the energy consumption for the driving situation and the wheel drive of the vehicle is specified based on the traveling or running to and running record collected from the vehicle, the said vehicle and of the same type in the at least one link Defining an energy curve and a reference energy curve representing a correlation between a vehicle driving situation and energy consumption and a reference correlation, and one or more specified driving situations of the energy curve with respect to the reference energy curve. A step of calculating the deviation and, if the deviation is negative, downhill Defining as said link characteristic of said at least one link, while defining as said link characteristic of said at least one link, if said deviation is positive, being uphill. To do.

According to the navigation server and the navigation method of the present invention, the traveling state of the vehicle and the energy consumption for driving the wheel specified based on the traveling record collected from the vehicle actually traveling or traveling on the link. Map information can be created in which the distinction between “uphill” and “downhill” defined in view of the quantity is included as the link characteristic of the link. For this reason, even when the map information is used and the vehicle in which the traveling state or the like is taken into consideration at the time of creating the map information is a vehicle having a different type or consumption mode of wheel driving energy, the energy consumption amount The prediction accuracy is improved.

  “Energy consumption” refers to the energy consumption for driving a wheel per unit distance or unit time, such as fuel consumption in a gasoline vehicle, electricity consumption in an electric vehicle, fuel consumption or electricity consumption in a hybrid vehicle, or fuel consumption rate in a fuel cell vehicle. Is conceptually included.

  In addition, each element which is a component of the present invention “recognizes” information means receiving information from an external information source, retrieving information from a database, reading information from a storage device or memory, receiving Alternatively, the information needs to be measured, calculated, estimated, judged, set, determined, etc., by calculating the basic signal retrieved, and stored in a storage device or memory. This means the execution of any arithmetic processing for preparing the information for the arithmetic processing to be performed.

  In the navigation server of the present invention, it is preferable that the link characteristic defining element is configured so as to define that the inclination angle is increased stepwise or continuously as the deviation is larger as the link characteristic.

  According to the navigation server having the configuration, in addition to the “uphill” and the “downhill” defined as described above, map information including the steepness / inclination of the link as the link characteristic of the link can be created. For this reason, by using the map information, the prediction accuracy of the energy consumption amount for driving the wheels of the vehicle can be further improved.

  In the navigation server of the present invention, it is preferable that the link characteristic defining element is configured to define the reference energy curve for each type of road constituted by the links.

  According to the navigation server of this configuration, the link characteristics are defined taking into account that the energy consumption of the vehicle differs depending on the type of road, such as general roads and expressways, or urban roads and suburban roads. Is done. For this reason, the use of the map information including the link characteristic further improves the prediction accuracy of the energy consumption for driving the vehicle wheels.

  In the navigation server of the present invention, the departure position, the destination position and the departure time of a vehicle having a wheel drive motor are recognized by communication with the navigation client, and the departure position is determined based on the departure position, the destination position and map information of the vehicle. Based on a server route search element configured to search a server route connecting a position and a target position, and the departure time and road traffic information, the prediction of the vehicle in each of at least a plurality of links constituting the server route A factor recognition element configured to recognize a travel time zone and a predicted travel situation of the vehicle in the predicted travel time zone, the link characteristics of each of the plurality of links included in the map information, and the predicted travel Based on the situation, the link characteristics, the running situation of the vehicle and the motor A power consumption prediction element configured to predict a total value of the power consumption when it is assumed that the vehicle completes the server route according to a prediction rule representing a correlation with power consumption. It is preferable.

  According to the navigation server of the configuration, the total power consumption for driving the wheel when it is assumed that the vehicle completes the first server route by considering the link characteristics of the plurality of links constituting the server route. The prediction accuracy of the value is improved.

  In the navigation server of the present invention, the server route search element searches for the plurality of server routes, and the total value of the power consumption predicted by the power consumption prediction element is the lowest among the plurality of server routes. It is preferable that the server route is selected as an eco route, and the navigation client is made to recognize the eco route and the power consumption in the eco route based on communication with the navigation client.

  According to the navigation server configured as described above, the vehicle guidance by the navigation client according to the server route (eco route) having the lowest total power consumption for driving the wheels among the plurality of server routes, and thus the power consumption. This prediction result can be effectively used in an electric vehicle or a hybrid vehicle.

1 is an overall configuration diagram of a navigation system as an embodiment of the present invention. Explanatory drawing regarding the map information creation function. The structure explanatory drawing of map information. Explanatory drawing regarding the definition method of a link characteristic. Explanatory drawing regarding the eco-route search function. Explanatory drawing about search of an eco route.

(Configuration of navigation system)
The navigation system according to the embodiment of the present invention shown in FIG. 1 includes a first navigation server 100, a second navigation server 200, and a navigation client 300 (navigation client). The first navigation server 100, the second navigation server 200, and the navigation client 300 are configured to be able to communicate with each other via an appropriate communication network (network) such as the Internet or a telephone communication network.

  The first navigation server 100 is configured to be able to communicate with the travel recording server via a designated communication network. The second navigation server 200 is configured to be able to communicate with each of a travel recording server, a weather information providing server (external environmental factor information providing server), and a traffic information server (not shown) via a designated communication network.

(Configuration of the first navigation server)
The first navigation server 100 includes a reference definition element 110, a link characteristic definition element 120, and a first server storage device 140 (map information database). Each of the elements 110 and 120 is configured to execute a responsible calculation process described later. “Constructed” so that the constituent elements of the present invention execute the assigned arithmetic processing means that a necessary program software and data are read from the storage device and the arithmetic processing is executed in accordance with the software or the like. It means that it is designed. The first server storage device 140 is configured to store map information (server map).

(Configuration of the second navigation server)
The second navigation server 200 includes a server route search element 210, a factor recognition element 220, a power consumption prediction element 230, and a second navigation server storage device 240. Each of the elements 210 to 230 is configured to execute a responsible calculation process described later. The second navigation server storage device 240 stores data and execution results necessary for execution of the arithmetic processing performed by the elements 210 to 230.

(Configuration of the navigation client 300)
The navigation client 300 includes, for example, a navigation device mounted on the vehicle X, a mobile terminal device such as a smartphone or a tablet terminal in which a navigation application is installed, a PC, or the like. The navigation client 300 includes an input device 301, an output device 302, and a client storage device 304.

  The input device 301 is configured by a push button type or touch panel type interface, and enables an information input operation such as designation input of a target position point by a user. You may be comprised by the audio | voice recognition apparatus which analyzes the acoustic signal collected by the microphone and the said microphone, and recognizes the user's utterance content or the information intended to input.

  The output device 302 is configured by an image output device including a liquid crystal panel on which a client map or the like is displayed. When the input device 301 includes a touch panel interface, a touch button is displayed on the image output device. The output device 302 may be configured by an audio output device that outputs an audio signal related to the image information displayed on the image output device in addition to the image output device.

  The client storage device 304 is configured to store a client map in which each point is described by latitude and longitude. According to the client map, a link connecting two separated points is described by a coordinate value group or a coordinate string representing a plurality of points on the link. At least some of the links may be described in association with link identification information for identifying the link or the type of road constituted by the link. In addition, the location ranges such as parks, rivers, and facility sites are described by coordinate strings representing a plurality of points on the contour lines or closed curves of the ranges. Each range may be described in association with the type of object located in the range.

  The client map may be the same map as the server map, or may be a map that differs in at least a part of the format from the server map.

  The navigation client 300 stores or cumulatively stores traveling records at predetermined time intervals during traveling. The navigation client 300 transmits the travel record accumulated at an appropriate timing to the first navigation server 100. The first navigation server 100 stores the received travel record in the travel record server.

  The “travel record” includes, for example, “own vehicle position (exactly the position of the navigation client 300)”, “measurement time of own vehicle position (every predetermined time)”, “link identifier for identifying each link” "The vehicle speed measured according to the output signal of the in-vehicle speedometer, the first type power consumption and the second type power consumption during the period from the previous measurement time to the current measurement time of the vehicle position, the indoor set temperature and the wiper Drive speed is included. The navigation client 300 separates the consumed power into the first type power consumption or the second type power consumption based on a signal received from the vehicle-mounted sensor or the like when accumulating the travel record.

(Navigation system functions)
(Map information creation function)
The “map information creation function” which is one of the functions of the navigation system having the above configuration, specifically, the first navigation server 100 will be described. As shown in FIG. 3, a configuration in which each link (or a link identifier for identifying the link) L (i) is associated with a link characteristic γ (i) for evaluating the degree of energy consumption. Map information is created.

  The link characteristic γ (i) represents an apparent inclination state of the link L (i). The “apparent inclination state” is defined according to the degree of travel difficulty of the vehicle or the amount of energy consumption for driving the wheels. The link characteristic γ (i) is used as a basis for predicting the power consumption of an electric vehicle, and is defined as a correction coefficient (details will be described later).

  For example, on a downhill road, the fuel consumption is generally low because the required propulsive force of the vehicle X is small, but the frequency of acceleration / deceleration increases because of poor visibility or many curves. The characteristic γ (i) of the downhill link L (i) is defined as a downhill whose degree of inclination is smaller than the actual gradient, i.e., close to flat, because the fuel consumption is relatively large, or very rare May be defined as uphill. On the other hand, as the required propulsive force of the vehicle X increases on the uphill, the fuel consumption generally increases. However, the uphill link L (i) where the frequency of acceleration / deceleration increases as described above. The characteristic γ (i) is defined as a slope having a greater degree of slope than the actual slope, while the upslope has a low frequency of acceleration / deceleration due to good visibility or a gentle curve. The characteristic γ (i) of the link L (i) may be defined as an uphill having a smaller degree of slope than the actual slope, that is, close to flat.

  The reference definition element 110 recognizes or collects “travel records” of a number of gasoline vehicles as the vehicle X based on communication with the navigation client 300 mounted on the vehicle X (FIG. 2 / STEP 102). The gasoline automobile is configured such that wheels are driven only by an internal combustion engine using gasoline as fuel, and does not include a hybrid vehicle.

  In the travel record of the vehicle X, the current measurement is performed at least from the “own vehicle position (more precisely, the position of the navigation client 300)”, “measurement time of the own vehicle position (every predetermined time)” and the previous measurement time of the own vehicle position “Gasoline consumption” in the period up to the time is included. The travel record may include an identifier for identifying the vehicle X, the vehicle type, the owner of the vehicle X, or the like.

  The reference definition element 110 recognizes the link L (i) traveled by the vehicle X (gasoline automobile) and the vehicle speed V when traveling the link L (i) based on the acquired travel record. By referring to the map information based on the latitude and longitude representing the vehicle position, the link L (i) where the vehicle was traveling at the measurement time of the vehicle position can be specified. The vehicle speed V (i) can be specified based on the measurement period of the vehicle position and the positions at the start and end of the period.

  A “link identifier” for identifying each link included in the navigation map used by the navigation client 300 mounted on each vehicle X is included in the travel record. Based on this, a link may be recognized. The vehicle speed measured according to the output signal of the in-vehicle speedometer may be included in the travel record.

  Based on the travel records collected from a large number of vehicles X, the correlation between the vehicle speed V and the fuel efficiency E is defined for each link L (i) (FIG. 2 / STEP 104).

  Specifically, the average fuel efficiency EA (V) of a large number of vehicles X is calculated for each link L (i) and for each vehicle speed V (or vehicle speed range), and the fuel efficiency curve E (i, V) is defined as the correlation. The plot representing the combination of the vehicle speed V and the average fuel efficiency EA (V) at the link L (i) is discrete, but an approximate curve representing the distribution form of the discrete plot is defined as the fuel efficiency curve E (i, V). Is done.

  Thus, for example, for the link L (1), a fuel consumption curve E (1, V) having a change characteristic as indicated by a one-dot chain line in FIG. 4 is defined as the correlation. Similarly, for the link L (2), a fuel consumption curve E (2, V) having a change characteristic as shown by a two-dot chain line in FIG. 4 is defined as the correlation.

  The reference definition element 110 defines a reference correlation based on the correlation between the vehicle speed V and the fuel efficiency E defined for each link L (i) (FIG. 5 / STEP 106). For example, the fuel efficiency curve E (i, V) is averaged for all links or all links for each road type, so that the reference fuel efficiency curve E (V) is calculated as the reference correlation. Thus, for example, a curve having a change characteristic as shown by a solid line in FIG. 4 is defined as the reference fuel consumption curve E (V).

  The link characteristic defining element 120 determines the link characteristic for each link L (i) according to the comparison result or the magnitude relationship between the fuel efficiency curve E (i, V) and the reference fuel efficiency curve E (V) for each link L (i). γ (i) is defined (FIG. 2 / STEP 108).

  For example, based on the deviation ΔE (i, V0) of the fuel efficiency E (i, V0) at the specified speed V0 of each link L (i) with respect to the reference fuel efficiency E (V0) at the specified speed V0, according to a predetermined definition rule. A link characteristic γ (i) is defined. As the designated speed V0, an average traveling speed or legal speed of the vehicle on the road type to which the link L (i) belongs can be adopted.

  In the example shown in FIG. 4, the fuel consumption deviation ΔE (1, V0) in the link L (1) is a positive value, and the fuel consumption deviation Δ (2, V0) in the link L (2) is a negative value. | ΔE (1, V0) | is larger than | ΔE (2, V0) |.

  For example, as shown in Table 1, the definition rule is a link defined by an apparent inclination state and a correction coefficient value (positive value) depending on the polarity of the deviation ΔE (i) and the magnitude of the absolute value. The characteristic γ (i) is defined to vary over five stages. γ (i) may be defined to fluctuate over a plurality of stages different from 5, or may be defined to fluctuate continuously.

  Even if an average value in a plurality of designated speeds or a predetermined speed range of the deviation ΔE (i, V) of the fuel efficiency E (i, V) with respect to the reference fuel efficiency E (V) is adopted as the basis for defining the link characteristic γ (i). Good. For example, in the example shown in FIG. 4, the integrated value of the deviation ΔE (i, V) in the domain [V1, V2] including the designated speed V0 is divided by the width (V2−V1) of the domain. The result may be calculated.

  Then, the link characteristic defining element 120 creates map information in which the link characteristic γ (i) is added to each link L (i) (FIG. 2 / STEP 110). Thereby, map information having a configuration as shown in FIG. 3 is created. The map information is stored in the first server storage device 140.

(Eco-route search function)
The navigation system configured as described above, specifically, the “eco-route search function” which is one of the functions of the second navigation server 200 will be described.

  In the navigation client 300 mounted on the electric vehicle as the vehicle X, the target position G is set by the user (FIG. 5 / STEP 301). The electric vehicle is configured to operate an on-vehicle device or an auxiliary device other than the electric motor, such as an electric motor (electric motor) for driving a wheel and an air conditioner, by electric power of an on-vehicle battery (not shown).

  The navigation client 300 steadily measures its own position (latitude and longitude) based on the GPS signal received from the artificial satellite and, if necessary, the output signal of the gyro sensor. A target position set time or a time after a predetermined time is defined as a departure time TS, and a measurement position at or near the target position set time is defined as a start position S.

  The navigation client 300 reads the internal environmental factor F2 representing the setting state of the driving state of the in-vehicle device from the memory, and transmits the departure position S, the destination position G, and the departure time TS to the second navigation server 200 (FIG. 5). / Arrow D1).

  In response to this, in the second navigation server 200, the server route search element 210 searches for a server route constituted by a plurality of links connecting the departure position S and the destination position G (FIG. 5 / STEP 202).

  When searching for a server route, map information (server map) stored in the first server storage device 140 is used. In addition to the map information, the server route may be searched using traffic information acquired from the traffic information server (eg, estimated travel time for each time zone in each link L (i)). As a result, for example, as shown in FIG. 6, a plurality of server routes R1 to RN having both end points in common are searched.

  Based on the external environmental factor F1 (i) and the internal environmental factor F2 (i) in each link L (i), the power consumption predicting element 230 is assumed that the vehicle X has completed the server route according to the first prediction rule. The assumed first type power consumption ΣP1 (i) is predicted or evaluated (FIG. 5 / STEP 204).

  The first type power consumption P (i) in each link L (i) is the power consumed for the operation of an in-vehicle device other than the electric motor (electric motor), such as an air conditioner, a window opening / closing actuator, and a wiper. Means predicted power.

  The external environment factor F1 (i) is a factor representing the environment of the vehicle X that affects the first type power consumption P1 (i). As the external environmental factor F1 (i), predicted weather information in an area to which the link L (i) belongs in a time zone in which the vehicle X is predicted to travel on each link L (i) constituting the server route is adopted. The The predicted weather information includes temperature, humidity, weather, amount of sunshine, amount of rainfall or snowfall, and whether or not there is snow or freezing on the road. The external environment factor F1 (i) is recognized by the factor recognition element 220 based on communication with the weather information server.

  The internal environmental factor F2 (i) is a factor representing the driving mode of the in-vehicle device different from the wheel driving motor. As the internal environmental factor F2 (i), a setting mode by the occupant of the vehicle X such as the in-vehicle temperature or the wiper driving speed adjusted by the operation of the air conditioner is adopted. The internal environmental factor F2 (i) is based on communication with the navigation client 300 by the factor recognition element 220, for example, communication at the time of recognition of the departure position S by the server route search element 210 (see FIG. 5 / arrow D1). Is recognized. The internal environmental factor F2 (i) is not defined for each link L (i), but may be uniformly defined for all the links L (i) for each vehicle X.

  The predicted travel time zone of the vehicle X in each link L (i) constituting the server route is the departure time TS of the vehicle X and the estimated travel time required in each time zone in each link L (i) as traffic information. Can be predicted.

  The “first prediction rule” represents a correlation between the external environmental factor F1 (i), the internal environmental factor F2 (i), and the first type power consumption P1 (i). The internal environmental factor F2 (i) and the first type power consumption P1 (i) of the vehicle X in each link L (i) collected from a large number of vehicles X (electric vehicle or hybrid vehicle) by the factor recognition element 220. The first prediction rule may be created on the basis of the measurement result and the measurement result of the external environmental factor F1 (i) in the same time zone stored in the weather information server. For example, the average value of the first type power consumption P1 (i) for each temperature or temperature range as the external environmental factor F1 (i) and for each vehicle temperature or vehicle temperature range as the internal environmental factor F2 (i) May be calculated, and a function or table representing the change characteristic of the average value with respect to each of the air temperature and the vehicle interior temperature may be derived as the first prediction rule.

  According to the first prediction rule, for example, as the “temperature” or “humidity” as the external environmental factor F1 (i) deviates from a normal value (for example, 20 [° C.] or 50%), the first type power consumption P1 (i) is greatly evaluated. This is because it is predicted that as the temperature is higher or lower, or as the humidity is higher, more electric power is consumed to operate the air conditioner. The low temperature may be expressed by the presence or absence of snow.

  From the same viewpoint, as the internal environmental factor F2 (i), the set value or the set average value of the vehicle interior temperature during the cooling operation of the air conditioner is lower than the reference temperature (for example, 24 [° C.]), or heating As the set value or set average value of the in-vehicle temperature during driving is higher than the reference temperature, the first type power consumption P1 (i) is evaluated higher. The set average value of the in-vehicle temperature may be calculated for each category of the external environmental factor F1 (i) such as by weather.

  The operation mode of the air conditioner varies depending on the amount of sunshine. Therefore, when the “temperature” as the first factor F1 (i) is low and the “sunshine amount” is small, the first type power consumption P (i) is larger than when the “temperature” is low and the “sunshine amount” is large. Be evaluated. When the “temperature” as the first factor F1 (i) is high and the “sunshine amount” is large, the first type power consumption P (i) is evaluated to be larger than when the “temperature” is high and the “sunshine amount” is small. .

  The larger the “rainfall” or “snowfall” as the external environmental factor F1 (i), the larger the predicted value of the first type power consumption P1 (i). This is because it is predicted that the greater the amount of rainfall or the amount of snowfall, the more power is consumed for the operation of the wiper.

  From the same viewpoint, the higher the set value or the set average value of the wiper driving speed as the internal environmental factor F2 (i), the higher the first type power consumption P1 (i) is evaluated. The set average value of the driving speed of the wiper may be calculated for each category of the external environmental factor F1 (i) such as the amount of rainfall.

  Based on the external environmental factor F1 (i), the first type power consumption P1 (i) according to the first prediction rule representing the correlation between the external environmental factor F1 (i) and the first type power consumption P1 (i) only. ) May be predicted.

  Based on the predicted traveling speed VP (i) in the time zone in which the vehicle X is predicted to travel on each link L (i) constituting the server route and the link characteristic γ (i), the power consumption prediction element 230 According to the “second prediction rule”, the second-type power consumption ΣP2 (i) when the vehicle X is assumed to have completed the server route is predicted or evaluated (FIG. 5 / STEP 206).

  The second type power consumption P2 (i) in each link L (i) means the power (predicted power) consumed for driving the wheel driving motor (electric motor). The predicted traveling speed VP (i) of the vehicle X in each link L (i) is recognized by the factor recognition element 220 based on communication with the traffic information server.

  The “second prediction rule” represents a correlation among the traveling speed V (i) of the vehicle X in each link L (i), the link characteristic γ (i), and the second type power consumption P2 (i). Based on the measurement results of the traveling speed V (i) and the second type power consumption P2 (i) of the vehicle X in each link L (i) collected from a large number of vehicles X (electric vehicles) by the factor recognition element 220. A second prediction rule may be created. For example, an average value of the second type power consumption P2 (i) is calculated for each different traveling speed or traveling speed range, and a function or table representing a change characteristic of the average value with respect to the traveling speed is derived as the second prediction rule. Also good.

  For example, the second type power consumption P2 (i) may be evaluated as f (V (i)) according to the function f representing the second prediction rule without considering the link characteristic γ (i). On the other hand, even if the second type power consumption P2 (i) is evaluated as γ (i) × f (V (i)) by considering the link characteristic γ (i) included in the map information. Good. Thereby, when the apparent inclination state of the link L (i) is an uphill, the second type power consumption P2 (i) is larger than when the apparent inclination state of the link L (i) is a downhill. Highly appreciated (see Table 1).

  The server route search element 210 is one in which the total value ΣP1 (i) + ΣP2 (i) of the first type power consumption P1 (i) and the second type power consumption P2 (i) among the plurality of server routes is minimized. The server route is selected as an “eco route” (FIG. 2 / STEP 208). Thereby, for example, one server route Rk is selected as the eco route among the N server routes R1 to RN shown in FIG. One server route that minimizes the total value ΣP2 (i) of the second type power consumption P2 (i) may be selected as the “eco route”.

  The server route search element 210 is based on the communication with the navigation client 300, and in addition to the eco route, the total value ΣP1 (i) of the first type power consumption P1 (i) and the total value of the second type power consumption P2 (i) The navigation client 300 is made to recognize ΣP2 (i) (FIG. 5 / arrow D2). In order to reduce the amount of communication information, each coordinate value group of a plurality of discrete points on the eco route may be transmitted to the navigation client 300. From the same viewpoint, some link identifiers among the plurality of links L (i) constituting the eco route may be transmitted to the navigation client 300.

  The navigation client 300 searches for a navigation server route as a reproduction result of the eco route based on the fragmentary information of the eco route. The navigation client 300 superimposes the navigation server route on the client map and displays it on the output device 302 (FIG. 5 / STEP 302). Thereby, for example, a client route that is the same as or substantially the same (similar) as the server route Rk shown in FIG. At this time, the total value ΣP1 (i) of the first type power consumption P1 (i) and the total value ΣP2 (i) of the second type power consumption P2 (i) are also output and displayed on the output device 302.

(Effect of navigation system)
According to the navigation system (particularly, the first navigation server 100) that exhibits the above functions, the traveling state (vehicle speed V (i)) and wheel driving of the vehicle X when the vehicle X actually travels on the link L (i). Map information including distinction between “uphill” and “downhill” defined in view of the energy consumption E (i) and the slowness of the slope as link characteristics of the link can be created (FIG. 2 / STEP102). To STEP 110 and FIG. For this reason, when the map information is used, the vehicle X (for example, gasoline automobile) in which the traveling state or the like is considered at the time of creating the map information is different from the vehicle X (for example, gasoline driving vehicle) in the type or consumption mode of the wheel driving energy. Even in the case of an electric vehicle or a hybrid vehicle, the prediction accuracy of the energy consumption (second-type power consumption E2 (i)) can be improved.

  The link characteristic γ (i) is defined in consideration of the difference in energy consumption of the vehicle X depending on the type of road, such as a general road and a highway, or a city road and a suburban road. For this reason, the map information including the link characteristic γ (i) is used, thereby further improving the prediction accuracy of the energy consumption for driving the wheels of the vehicle X (the second type power consumption E2 (i)). Figured.

  According to the navigation system (particularly the second navigation server 200) that exhibits the above function, the server route Rk that connects the starting position S and the destination position G of the vehicle X (electric vehicle or hybrid vehicle) having a wheel driving motor. A time zone in which each of the plurality of links L (i) included in (see FIG. 6) travels is predicted based on the departure time TS of the vehicle X and road traffic information. Then, the external environmental factor F1 (i) in the predicted time zone is recognized. For this reason, while the vehicle X travels from the starting position S toward the destination position G, it is possible to improve the prediction accuracy of the external environmental factor F1 (i) that contacts each of the plurality of links L (i). .

  Therefore, based on the external environment factor F1 (i), according to the first prediction rule, the prediction accuracy of the total value ΣP1 (i) of the first type power consumption when assuming that the vehicle X completes the server route Rk is improved. Is planned. As a result, the prediction accuracy of the total power consumption of the vehicle X including the first type power consumption is improved.

  In addition to the external environmental factor F1 (i), the first type power consumption ΣP1 (i) is obtained after taking into account the internal environmental factor F2 (i) representing the setting state of the operating state of the in-vehicle device such as an air conditioner. Predicted (see FIG. 5 / STEP 204). For this reason, the prediction of the driving state of the in-vehicle device in each time zone in consideration of the passenger's preference of the vehicle X reflected in the setting mode (preferably higher interior heating temperature or lower interior cooling temperature). The accuracy, and thus the prediction accuracy of the first type power consumption ΣP1 (i) can be improved.

  The first prediction rule is created based on the measurement result of the first type power consumption P1 (i) collected from the vehicle X (electric vehicle) actually traveling on the link L (i). Thus, by following the first prediction rule reflecting the actual experience value of the vehicle X, the prediction of the first type power consumption P1 (i) when an arbitrary electric vehicle travels on each link L (i). The accuracy is improved.

  According to the predicted traveling state (predicted vehicle speed VP (i)) and the link characteristic γ (i) of the vehicle X based on the road traffic information in each of the plurality of links L (i) constituting the server route Rk, the vehicle X A total value ΣP2 (i) of the second power consumption when it is assumed that the server route Rk is completed is predicted (see FIG. 5 / STEP 206). As a result, the prediction accuracy of the second power consumption P2 (i) is improved, and consequently the prediction accuracy of the total power consumption or power consumption of the vehicle X including the second power consumption P2 (i) is improved.

  The second prediction rule is created based on the measurement result of the second type power consumption P2 (i) collected from the vehicle X (electric vehicle) that actually traveled on the link L (i). Thus, according to the second prediction rule reflecting the actual experience value of the vehicle X, the prediction of the second type power consumption P2 (i) when an arbitrary electric vehicle travels on each link L (i) The accuracy is improved.

  Among the plurality of server routes R1 to RN (see FIG. 6), the total value ΣP2 (i) of the second type power consumption is in accordance with the lowest server route Rk (eco route) and It is possible to effectively use the guidance, and thus the prediction result of the power consumption, in the electric vehicle or the hybrid vehicle.

DESCRIPTION OF SYMBOLS 100 ... 1st navigation server, 110 ... Reference | standard definition element, 120 ... Link characteristic definition element, 140 ... 1st server storage device (map information database), 200 ... 2nd navigation server, 210 ... Server route search element, 220 ... Factor Recognition element 230... Power consumption prediction element 240. Second server storage device 300 Navi client X X vehicle

Claims (6)

A navigation server configured to create map information including link characteristics specific to a link for at least one link among a plurality of links constituting the road,
Based on the energy consumption for the driving situation and the wheel drive of the vehicle is specified based on the collected travel records from the at least one is actually traveling or running vehicle links, wherein at least one of A reference definition element configured to define an energy curve and a reference energy curve representing a correlation and a reference correlation between a driving situation and energy consumption of a vehicle of the same type as the vehicle at the link; and
A deviation of the energy curve with respect to the reference energy curve in one or a plurality of designated driving situations is calculated, and when the deviation is negative, a downhill is defined as the link characteristic of the at least one link On the other hand, comprising: a link characteristic defining element configured to define an uphill as the link characteristic of the at least one link when the deviation is positive Navigation server. The navigation server according to claim 1, wherein
The navigation server, wherein the link characteristic defining element is configured to define, as the link characteristic, that the inclination angle increases stepwise or continuously as the deviation increases. In the navigation server according to claim 1 or 2,
The navigation server, wherein the link characteristic defining element is configured to define the reference energy curve for each type of road constituted by the links. In the navigation server according to any one of claims 1 to 3,
A server that recognizes a departure position, a target position, and a departure time of a vehicle having an electric motor for driving a wheel by communicating with a navigation client, and connects the departure position and the target position based on the departure position, the target position, and map information of the vehicle A server route search element configured to search for a route; and
Based on the departure time and road traffic information, the vehicle is configured to recognize the predicted travel time zone of the vehicle at each of a plurality of links constituting the server route and the predicted travel status of the vehicle in the predicted travel time zone. Factor recognition elements
Based on the link characteristics and the predicted travel status of each of the plurality of links included in the map information, according to a prediction rule representing a correlation between the link characteristics, the travel status of the vehicle, and power consumption by the motor, A navigation server, further comprising: a power consumption prediction element configured to predict a total value of the power consumption when it is assumed that the vehicle completes the server route. The navigation server according to claim 4, wherein
The server route search element searches a plurality of the server routes, and selects a server route having the lowest total power consumption predicted by the power consumption prediction element as the eco route among the plurality of server routes. A navigation server configured to cause the navigation client to recognize the eco route and the power consumption in the eco route based on communication with the navigation client. A method of creating map information including link characteristics specific to a link for at least one link among a plurality of links constituting a road,
Based on the energy consumption for the driving situation and the wheel drive of the vehicle is specified based on the collected travel records from the at least one is actually traveling or running vehicle links, wherein at least one of Defining an energy curve and a reference energy curve representing a correlation and a reference correlation between a driving situation and energy consumption of a vehicle of the same type as the vehicle at the link; and
Calculating a deviation of the energy curve with respect to the reference energy curve in one or more designated driving situations;
When the deviation is negative, it is defined as the link characteristic of the at least one link as being downhill, while when the deviation is positive, it is defined as being an uphill when the deviation is positive. And defining the link characteristic as a navigation method.